AI Data Verification Platform for Risk Assessment
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Solution Overview
Problem
Conventional software solutions for electronic content verification are inefficient due to irrelevant questions, reliance on subjective human reviewer skills, and time-consuming manual review processes, which hinder effective identification of potential risks.
Innovation Solution
An intelligent data verification platform utilizing a suite of AI models that work together to predict relevant questions, provide answers, and determine risks, allowing human reviewers to collaborate with AI for efficient and transparent risk analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional software solutions use predetermined questions for manual review, then human reviewers can systematically evaluate data, but the process becomes inefficient due to irrelevant questions and subjective reviewer skills
Solution Approach 1:
The system enables automated self-service risk assessment by training AI models to independently evaluate data without requiring human reviewers to answer predetermined questions. The model automatically identifies relevant risk factors and generates assessments, eliminating the inefficiency of manual review while maintaining consistent evaluation criteria.
Solution Approach 2:
The system dynamically adjusts evaluation parameters by training the AI model on historical review data to identify which questions and data points are actually relevant for risk assessment. This transforms the static predetermined question list into a dynamic, data-driven evaluation framework that adapts to specific cases.
2Measurement precision
If human reviewers manually review all data points, then comprehensive risk analysis can be performed, but the process is time-consuming and heavily relies on subjective skills
Solution Approach 1:
The system extracts only the most relevant features and data points for risk assessment by training the AI model to identify patterns in historical review data. This eliminates the need for reviewers to examine all data points, focusing attention only on the critical factors that actually predict risk outcomes.
Solution Approach 2:
The AI model performs preliminary risk assessment by pre-processing and evaluating data before human review. This preliminary action filters out low-risk cases and prepares targeted recommendations, allowing human reviewers to focus only on complex or high-risk cases that require subjective judgment.
3Ease of manufacture
If a static algorithm generates risk scores, then the process is simple to implement, but it cannot adapt to new data trends or emerging risks
Solution Approach 1:
The system transitions from static algorithm to dynamic machine learning model that continuously learns from new data. The model is retrained periodically on updated historical review data, allowing it to adapt to emerging risk patterns while maintaining the simplicity of automated score generation.
Solution Approach 2:
The system implements feedback loops where actual review outcomes and risk confirmations are fed back into the training data. This allows the model to learn from real-world performance and continuously improve its risk identification capabilities, adapting to new threats while maintaining implementation simplicity through automated retraining.
Data Source
AI summary
A method comprises determining whether a decision can be determined for the request based on a current information available; when the decision can be determined, utilizing a first model to determine a set of questions corresponding to the request, the first model previously trained using training data comprising a set of questions associated with a set of requests; utilizing a second model to determine one or more predicted answers for the set of questions, the second model ingesting the set of questions determined by the first model and at least one attribute associated with the request to generate the one or more predicted answers; and utilizing a third model to determine the decision for the request.